This study investigates how the online behaviors of students can be associated with student outcomes via a learning analytics approach. The objective of this study is to examine whether online behaviors of students can predict student success in an English course. Also, this study aims to examine whether some machine learning techniques performed better than others. Learners in this study were students in an English for Academic Purposes course at a university in Hong Kong. Other than face-to-face teaching, this course included a mandatory multimodal language learning package first introduced in 2012. The research team retrieved a mega-dataset from all learners who took the course from 2012 to 2019 (n = 17,968). Using the Classification Tree technique, Logistical Regression, and Artificial Neural Networks, the research team derived three predictive algorithms to associate various online engagement variables (e.g., start day, first attempt scores, and number of attempts) with course success. Results suggest that Classification Tree performed better in predictive accuracy and F1 ratio. The chapter provides useful insights for adaptive design. The study also discusses several useful suggestions on how practitioners can adopt these algorithms for better adaptive design. Copyright © 2024 selection and editorial matter, Patsy D. Moskal, Charles D. Dziuban, and Anthony G. Picciano; individual chapters, the contributors.

In Hong Kong higher education, students' learning outcomes are increasingly treated as evidence to inform course and teaching improvement. Therefore, outcome-based teaching and learning (OBTL) has been encouraged by the University Grants Committee (UGC) since 2007. OBTL has gradually been implemented by Hong Kong higher education institutions (HEIs) to enhance student learning outcomes. Relating OBTL to the social cohesion/regulation matrix, this chapter aims at analyzing how OBTL is being implemented by the HEIs in Hong Kong. Given the high institutional autonomy and academic freedom afforded to individual HEIs, each university has established its own systematic framework for integrating outcome-based approaches into its teaching, learning, and assessment. Unlike other higher education systems in Asia with strong government supervision, the government in Hong Kong acts as an enabler and facilitator, leaving the UGC to invite international experts as an independent audit body to assure the quality of student learning. As a result, this chapter chooses the eight UGC-funded universities to investigate how they engage their faculty members in OBTL, and what the enabling and hindering factors are. Based upon the social cohesion/regulation matrix, the Hong Kong higher education system is featured by the individualist way of promoting OBTL. Nonetheless, while universities are empowered with institutional autonomy to decide upon teaching, and student learning matters, their strong orientation with OBTL means they cannot simply do whatever they like. Adopting a robust quality assurance mechanism in evaluating university performance through University Accountability Agreements, the institutional autonomy that universities enjoy rests heavily upon their performance in teaching and student learning, which is assessed through rigorous international benchmarking via the Quality Assurance Audit conducted by the UGC and research performance through the Research Assessment Exercise.

This chapter discusses the development and role of graduate education within a liberal arts college context by examining the case of Lingnan University, Hong Kong. With a student-centered focus, Lingnan University applies evidence-informed teaching and experiential learning to integrate liberal arts education, graduate programs, and research to enhance students’ learning experiences and maintain the core of whole-person education. Lingnan University’s efforts demonstrate the potential of developing graduate education in the liberal arts context, which can serve as implications for other liberal arts higher education institutions. Copyright © 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

The outbreak of the COVID-19 pandemic in early 2020 has brought tremendous challenges to higher education teaching and learning. Much attention has been put on online course construction and delivery to ensure the effectiveness of online learning. However, higher education assessment deserves more attention as it is also significantly impacted by the sudden switch from face-to-face to online. To examine how higher education institutions responded to the challenges in higher education assessment during the pandemic, this chapter conducted a case study of postgraduate programs at Lingnan University (LU) in Hong Kong to explore an appropriate assessment framework for online learning in a liberal arts education context. Through a quantitative approach, this study surveyed taught postgraduate students to explore the effectiveness, efficiency, and fairness of the assessment approaches applied by LU faculty members in the online and hybrid-mode classes. This case study aims to provide insights for improving higher education assessment under the pandemic crisis. Copyright © 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

This paper focused on students’ learning experiences in a flipped data science class integrated with peer instruction and just-in-time teaching. University students in Hong Kong participated in the research during the pandemic. Students’ perceptions of the flipped learning mode were investigated by a 5-point Likert scale questionnaire. According to the results, most students felt they enjoyed learning with the flipped mode since it allowed them to learn flexibly and independently. The course materials (i.e., instructional videos, lecture notes, and in-class exercises) were well-designed, and students perceived the materials were useful and user-friendly. As suggested by the students, more time should be given to the pre-class learning activities. Based on the suggestions, the researchers provide practical implications on improving teaching in the flipped science class. This study demonstrates how to flip a data science class and proves its value on students’ learning, providing implications for using flipped learning during the pandemic period. Copyright © 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG.

This chapter discusses a service learning course using combined board game activities and direct social skill teaching to enhance social and communicative skills of students with autism spectrum disorder (ASD). Ten students with ASD/social and emotional problems (aged 6–8) were invited to participate in an 8-week after-school training programme in a Hong Kong ordinary school. Each session was divided into two parts with 30 min of direct social skills instruction conducted by a teacher, followed by 60 min of board games facilitated by undergraduate students who were attending a social service learning course in a university. Most students with ASD engaged in playing board games and could apply the social and communicative skills that they learned in the training programmes. Facilitators also reported an increase in comprehension skills of students with ASD. Copyright © 2022 Springer Nature Singapore Pte Ltd.